| Download | - View final version: Machine learning for the prediction of safe and biologically active organophosphorus molecules (PDF, 536 KiB)
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| DOI | Resolve DOI: https://doi.org/10.21428/594757db.7b542d48 |
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| Author | Search for: Hu, Hang1; Search for: Ooi, Hsu Kiang1; Search for: Ghaemi, Mohammad Sajjad1; Search for: Hu, Anguang |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | 36th Canadian Conference on Artificial Intelligence, June 5-9, 2023, Montreal, QC, Canada |
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| Subject | molecule design; organophosphorus molecule; recurrent neural networks; self-attention |
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| Abstract | Drug discovery is a complex process with a large molecular space to be considered. By constraining the search space, the fragment based drug design is an approach that can effectively sample the chemical space of interest. Here we propose a framework of Recurrent Neural Networks (RNN) with an attention model to sample the chemical space of organophosphorus molecules using the fragment-based approach. The framework is trained with a ZINC dataset that is screened for high druglikeness scores. The goal is to predict molecules with similar biological action modes as organophosphorus pesticides or chemical warfare agents yet less toxic to humans. The generated molecules contain a starting fragment of PO2F but have a bulky hydrocarbon side chain limiting its binding effectiveness to the targeted protein. |
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| Publication date | 2023-06-05 |
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| Publisher | Canadian Artificial Intelligence Association |
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| Licence | |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 65c47bd7-003b-4165-a109-46b6b99341c8 |
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| Record created | 2023-07-07 |
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| Record modified | 2023-07-10 |
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